1. Field of the Invention
The present invention relates to an image process technique, and in particular to a method for restoring a compressed image by using a hybrid motion compensation discrete cosine transform (hybrid MC/DCT) mechanism, and an apparatus therefor.
2. Description of the Background Art
In general, image compression techniques, such as MPEG1 and MPEG2 employ a hybrid motion compensation discrete cosine transform (hereinafter, referred to as “hybrid MC/DCT”) mechanism in order to improve compression efficiency. The hybrid MC/DCT mechanism is roughly divided into an encoding process and a decoding process. In the encoding process, an original image is divided into a plurality of blocks in order to compress information in a spatial section, a second-dimensional discrete cosine transform is performed on each block, and redundancy information in the image or between the images is reduced by using the correlation on a time axis among the images in order to decrease information in a temporal section. In the decoding process, the steps of the encoding process are performed in a reverse order. An encoder and a decoder are necessary to carry out the hybrid MC/DCT mechanism.
FIG. 1 is a block diagram illustrating an image encoder according to a related art. As shown therein, an input image signal is subtracted from an image signal moved from and compensated by an image memory 9, passed through a first switching unit 2, and inputted to a DCT unit 3. The DCT unit 3 performs a discrete cosine transform on the inputted image signal. A quantization unit 4 quantizes the image signal, and outputs a DCT coefficient (q). An inverse quantization unit 6 inversely quantizes the DCT coefficient (q), and an inverse DCT unit 7 carries out an inverse discrete cosine transform thereon, thereby restoring the original image signal. The restored image signal is added to an image signal restored in a previous stage by an adder 8, and inputted to an image memory 9. A controller 5 controls switching of the first and second switching units 2, 10, and transmits INTRA/INTER information (p=mtype; flag for INTRA/INTER), transmission information (t; flag for transmitted or not), and quantization information (qz=Qp; quantizer indication) to a decoder (not shown in FIG. 1). The image memory 9 outputs a motion vector information (v=MV; motion vector) to the decoder. The DCT unit 3 outputs the DCT coefficient (q) to the decoder.
However, information of the original image signal is lost during the process of coding the image signal described above, especially during the quantization process, thereby causing blocking artifacts and ringing effects to the image which is reconstructed in the decoder. The blocking artifacts imply irregularity between the blocks generated due to information loss resulting from the quantization of the low-frequency DCT coefficients, and the ringing effects result from quantization errors of the high-frequency DCT coefficients.
That is, in accordance with a coding technique using the DCT in a coding system of a static image or dynamic image, an image is divided into a plurality of blocks, and the DCT is performed on each block. On the other hand, when the DCT is carried out on the original image, its important information is mainly included in low-frequency elements, and becomes lesser in high-frequency elements. Furthermore, the low-frequency elements include a lot of information relating to adjacent blocks. The DCT does not consider the correlation between the blocks, and quantizes the low-frequency elements by blocks, thereby destroying continuity of the adjacent blocks. It is called the blocking artifacts.
In addition, when the coefficients obtained by performing the DCT are quantized, as a quantization interval is increased, the elements to be coded are decreased, and thus the number of the bits to be processed is reduced. As a result, the information of the high-frequency element included in the original image is reduced, thereby generating distortion of the reconstructed image. It is called the ringing effects. The ringing effects generated by increasing the quantization interval are serious especially in a contour of an object in the reconstructed image.
As techniques for removing the blocking artifacts and the ringing effects, employed are a low pass filtering method and a regularized image restoration method.
According to the low pass filtering method, a plurality of pixels around a predetermined pixel are selected, and an average value thereof is computed. Here, a filter tap or filter coefficients are set by experience. For example, referring to FIG. 2, there is provided a block of N*N size. Reference numerals A to F depict pixels. Pixels C, D are adjacent to a boundary of the block. In order to reduce irregular variations between the pixels C, D, a k-tap (here, 7-tap) filtering is performed, and a threshold value replacing a D pixel value is computed according to local statistics. There is an advantage in that a computation amount is reduced by utilizing a predetermined threshold value according to the comparison with the local statistics. However, an adaptive processing power in accordance with a quantization parameter is deficient, and thus a screen quality of the restored image is excessively smoothed according to the kind of the images and compression ratio.
The regularized image restoration method adaptively deals with the blocking artifacts in accordance with statistical properties of the image. That is, irregular information around the boundary of the block or in the block is all computed. However, the computed values form a matrix shape, and thus a real time processing is difficult due to the great computation amount. In addition, an average value obtained by a computation result of the irregular information is equally applied to the pixels, regardless of a degree of irregularity. Accordingly, when a block has a high degree of irregularity, it can be reduced. However, in case of a block having a low degree of irregularity, it may be increased. Thus, the system is not adaptive. Also, the information in the temporal section is not processed, and thus irregularity between the images cannot be adaptively processed.